Data Science Job Roles Across Organizational Levels

Data scientists work at all levels of the organization. Our survey of data professionals revealed that Director-level data scientists have the highest level of proficiency across many data science skills and work with more of their peers compared to data scientists who are Individual Contributors, Managers or even Executives. Satisfaction with outcome of analytics projects did not differ across job levels.

Even though data science is a relatively new field, you can find data scientists at all levels of an organization, from individual contributors (who likely work on a team) to directors and executives who lead the company's data science efforts. In this post, I examined data scientists across different organizational levels to understand their differences and similarities.

Figure 1. Data Science Roles across Organizational Levels

In our study of data scientists, we asked over 520 data professionals about their level of proficiency across 25 data science skills (in 5 areas), their job level (individual contributor, manager, director and executive), the size of their team and satisfaction with their work outcome. We found that most of the respondents were individual contributors (N = 342) and managers (N = 91). Only a handful were directors (N = 15) and executives (N = 19).

Data science roles varied over job level (see Figure 1). In general, Business Management data scientists are more prevalent higher up in the organization (i.e., Executives and Directors) while Research, Creative and Developer data scientists are more prevalent at lower organizational levels (i.e., Managers and Individual Contributors).

Figure 2. Proficiency in 5 data science skills across job levels.

Data scientists who were in a Director-level position reported the highest levels of proficiency in Business, Programming, Math & Modeling and Statistics compared to data scientists in other job levels (see Figure 2). Surprisingly, data scientists who were Executives showed the lowest levels of proficiency in Statistics and Math & Modeling.

When looking at satisfaction with work outcome, Directors reported the highest satisfaction among the four job levels, but the difference was not statistically significant (see Figure 3). The non-significant results could simply reflect the relatively small sample sizes for a couple of the groups (i.e., Executives and Directors). We will examine these differences as we acquire more data.

Data scientist who were Directors worked with significantly more of their data science peers on analytics projects compared to data scientists from other job levels. Director-level data scientists indicated they worked with nearly 4 other peers (3.9) while the other groups worked with fewer of their peers (Executives: 2.6 peers; Managers: 3.0 peers; Individual contributors: 1.9 peers).

Summary

Director-level data scientists, who self-identified as primarily Business Management-type, had the highest level of proficiency across many data science skills compared to data scientists at other levels of the organization, including Individual Contributors, Managers and even Executives. Director-level data scientists, compared to their counterparts, worked with more of their peers on analytics projects (worked with around 4 other people).

While data science is about finding insights in data, for business, it is about monetizing that insight into a going concern. It is not surprising that most of the upper-level positions in data science are about Business Management. Business is about making money, after all. Data scientists who can transform the results of algorithms and regressions into a monetized product or service will likely rise to the organization's top data science positions.

Trackbacks/Pingbacks

[…] Director-level data scientists have the highest level of proficiency across many data science skills and work with more of their peers compared to data […]

About me

I am Business Over Broadway (B.O.B.). I like to solve problems through the application of the scientific method. I use data and analytics to help make decisions that are based on fact, not hyperbole. My interests are at the intersection of customer experience, data science and machine learning. To learn more about me and what I do, click here.